text: import os
  import cv2
  import time
  import base64
  import numpy as np
  
  __all__ = ['base64_to_cv2', 'Processor']
  
  
  def check_dir(dir_path):
      # 目录检查函数
      if not os.path.exists(dir_path):
          os.makedirs(dir_path)
      elif os.path.isfile(dir_path):
          os.remove(dir_path)
          os.makedirs(dir_path)
  
  
  def base64_to_cv2(b64str):
      # base64转cv2函数
      data = base64.b64decode(b64str.encode('utf8'))
      data = np.fromstring(data, np.uint8)
      data = cv2.imdecode(data, cv2.IMREAD_COLOR)
      return data
  
  
  class Processor():
      # 初始化函数
      def __init__(self, images=None, paths=None, batch_size=1, output_dir='output'):
          # 变量设置
          self.num_points = 21
          self.inHeight = 368
          self.threshold = 0.1
          self.point_pairs = [[0, 1], [1, 2], [2, 3], [3, 4], [0, 5], [5, 6], [6, 7], [7, 8], [0, 9], [9, 10], [10, 11],
                              [11, 12], [0, 13], [13, 14], [14, 15], [15, 16], [0, 17], [17, 18], [18, 19], [19, 20]]
  
          self.images = images
          self.paths = paths
          self.batch_size = batch_size
          self.output_dir = output_dir
  
          # 获取原始输入数据
          self.datas = self.load_datas()
  
          # 对原始输入数据进行预处理
          self.input_datas = self.preprocess()
  
      # 读取数据函数
      def load_datas(self):
          datas = []
  
          # 读取数据列表
          if self.paths is not None:
              for im_path in self.paths:
                  assert os.path.isfile(im_path), "The {} isn't a valid file path.".format(im_path)
                  im = cv2.imread(im_path).astype('float32')
                  datas.append(im)
  
          if self.images is not None:
              datas = self.images
  
          # 返回数据列表
          return datas
  
      # 数据预处理函数
      def preprocess(self):
          input_datas = []
  
          # 数据预处理
          for i, img in enumerate(self.datas):
              img_height, img_width, _ = img.shape
              aspect_ratio = img_width / img_height
              inWidth = int(((aspect_ratio * self.inHeight) * 8) // 8)
              inpBlob = cv2.dnn.blobFromImage(
                  img, 1.0 / 255, (inWidth, self.inHeight), (0, 0, 0), swapRB=False, crop=False)
              input_datas.append(inpBlob)
  
          # 数据按batch_size切分
          input_datas = np.concatenate(input_datas, 0)
          split_num = len(self.datas) // self.batch_size + 1 if len(self.datas) % self.batch_size != 0 else len(
              self.datas) // self.batch_size
          input_datas = np.array_split(input_datas, split_num)
  
          # 返回预处理完成的数据
          return input_datas
  
      # 结果后处理函数
      def postprocess(self, outputs, visualization):
          all_points = []
  
          # 结果后处理
          for im_id, img in enumerate(self.datas):
              points = []
              for idx in range(self.num_points):
                  probMap = outputs[im_id, idx, :, :]
                  img_height, img_width, _ = img.shape
                  probMap = cv2.resize(probMap, (img_width, img_height))
                  minVal, prob, minLoc, point = cv2.minMaxLoc(probMap)
  
                  if prob > self.threshold:
                      points.append([int(point[0]), int(point[1])])
                  else:
                      points.append(None)
  
              all_points.append(points)
  
              # 结果可视化
              if visualization:
                  # 检查输出目录
                  check_dir(self.output_dir)
                  # 结果可视化
                  self.vis_pose(img, points, im_id)
  
          # 返回后处理结果
          return all_points
  
      # 结果可视化
      def vis_pose(self, img, points, im_id):
          # 根据结果绘制关键点到原图像上
          for pair in self.point_pairs:
              partA = pair[0]
              partB = pair[1]
  
              if points[partA] and points[partB]:
                  cv2.line(img, tuple(points[partA]), tuple(points[partB]), (0, 255, 255), 3)
                  cv2.circle(img, tuple(points[partA]), 8, (0, 0, 255), thickness=-1, lineType=cv2.FILLED)
  
          # 可视化图像保存
          cv2.imwrite(os.path.join(self.output_dir, '%d_%d.jpg' % (im_id, time.time())), img)
  